This pilot study examines the effect of a novel decision support system in medical image interpretation. This system is
based on combining image spatial frequency properties and eye-tracking data in order to recognize over and under
calling errors. Thus, before it can be implemented as a detection aided schema, training is required during which SVMbased
algorithm learns to recognize FP from all reported outcomes, and, FN from all unreported prolonged dwelled
regions. Eight radiologists inspected 50 PA chest radiographs with the specific task of identifying lung nodules. Twentyfive
cases contained CT proven subtle malignant lesions (5-20mm), but prevalence was not known by the subjects, who
took part in two sequential reading sessions, the second, without and with support system feedback. MCMR ROC DBM
and JAFROC analyses were conducted and demonstrated significantly higher scores following feedback with p values of
0.04, and 0.03 respectively, highlighting significant improvements in radiology performance once feedback was used.
This positive effect on radiologists' performance might have important implications for future CAD-system
development.